Water management and water reuse strategies play a critical role in minimizing the costs, risks, and social and environmental impacts associated with the development of an unconventional oil and gas field. An effective water management strategy is also essential to maintain a social license to operate. However, most companies are forced to make water management decisions based solely on the flexibility that the water management strategy accommodates rapid changes and uncertainty in the drilling and hydraulic fracturing schedule. In nearly every situation this option is the most expensive and has the greatest environmental and social risks. Water infrastructure modeling software enables users to visualize several drilling and hydraulic fracturing schedules and water management strategies for different development plan alternatives. The user can quantitatively compare key outputs—such as costs, flexibility, probability of a traffic accident and CO2 emissions—for different potential scenarios. The modeling approach is applied to an integrated development plan to compare the costs and other key outputs for several water management and water reuse strategies. This method reduces the time and cost required from users while providing more accurate and flexible water management and water reuse strategies.
Water Management Strategy
Water management decisions are often challenging because they are directly influenced by interrelated and unknown considerations, as described in Figure 1. An ideal water management and water reuse strategy must be flexible to accommodate these considerations and be able to adapt to rapidly changing drilling and completion schedules. This requires an understanding of the spatial and temporal variability of water volumes within a specific field as well as the infrastructure and transportation requirements.
A software tool allows operators to compare different water management and water reuse scenarios based on the specific concerns and risks relating to the field (see Figure 2). This tool allows users to input a range of proposed drilling and completion schedules along with different water management strategies to compare strategies in the early planning stages. To develop the custom user interface, the software provider works closely with companies to collect data relating to a specific field or region. Typically, this data includes flowback and produced water volumes and specific well characteristics such as location, spud date, shut-in period, water use, target formation and number of hydraulic fracturing stages. The data are then correlated with the specific well characteristics to determine which ones have significant impacts on the volume of water required and the volume of flowback and produced water. The significant characteristics are used to model anticipated water volumes. The number of hydraulic fracturing stages or the measured depth of the well typically has the most significant impact on both water requirements and the volume of flowback and produced water. The water volumes have shown a relatively linear correlation to the number of hydraulic fracturing stages.2 Flowback and produced water is more difficult to model than the anticipated water requirements because of the different chokes used and shut-in periods for each well. An empirical decline curve is used to model the produced water volumes after the initial production data is filtered and smoothed. The volume of water required and the volume of flowback and produced water can be estimated for the entire field or region by summing the decline curve analysis for each well. The interface allows the user to quickly change modeling assumptions and drilling/completion schedules to understand how the water volumes in the field will change. Furthermore, the user can graphically visualize temporal and spatial water requirements in the changing field. This visualization is used to better plan when and where water infrastructure is required and to determine the treatment capacity and the type of treatment required.
The user can select the number, capacity and location of freshwater resources, injection wells and treatment facilities within the software interface. The software will quickly calculate key outputs, such as estimated costs, truck miles driven and percent of water reused based on adjustable assumptions and specific key outputs identified by the company. The user can virtually move water infrastructure within the field to compare key outputs for different scenarios. In addition, the number of truck miles can be further used to quantify other key parameters, such as the probability of an accident or spill for each scenario. Once a water management and water reuse strategy is selected, a range of drilling/completion schedules are used along with a range of variables, such as trucking and disposal costs, to see how the strategy may be affected in the future if the assumptions change. By quantifying the decision-making process, the user can make more informed choices about water management and reuse in the early planning stages. Making the best decision at this point can dramatically reduce the total capital and operating costs and the risk for the project.
For example, in most regions the volume of flowback and produced water will not meet the future nearby drilling and completion requirements, even if it is all treated and reused. Freshwater will be required to supplement the treated flowback and produced water. In these areas, less expensive treatment options may be used to meet the reuse requirements if the anticipated dilution ratio is understood. By modeling the anticipated dilution ratios in a field, these cost-savings measures can be implemented. The flexibility of the graphical user interface allows the user to instantly see the impact water management decisions have on the field. For example, if the user is considering piping the flowback and produced water to mobile treatment facilities because he or she is concerned about the price of disposal using Class II injection wells, the model can quickly show the user both scenarios to determine what the price increase, rate of development and cost of treatment will need to be to make a rational water management decision. Currently, these water management comparisons are made on a case-by-case basis with tedious calculations. By speeding up and quantifying the decision-making process, several scenarios and strategies can be rapidly compared, and the length of the engineering design and planning stages can be decreased dramatically.
The Importance of Water
Water is typically the single largest operating material for unconventional oil and gas wells by volume and directly impacts the social, environmental and economic risks. Optimizing water management is essential to minimizing these risks, reducing operating costs, and continuing to operate in populated and/or water scarce regions. A simple brute-force solution that relies on using a single centralized treatment facility for a field cannot provide the flexibility required to operate in rapidly changing oil and gas fields. Furthermore, mobile treatment facilities within a field are often too costly to implement. An optimized treatment strategy for most regions will likely include a combination of both large, cost-effective centralized treatment facilities and flexible mobile treatment facilities that can be easily deployed throughout the field. A software solution gives the operator the ability to visualize, model and quantify water volumes and qualities throughout a field based on flexible development plans. Water management scenarios can be modeled with the development plans to assess the efficiency and impacts of each scenario.
- Devin L Shaffer et al. "Desalination and Reuse of High-Salinity Shale Gas Produced Water: Drivers, Technologies, and Future Directions." Environmental science & technology 47.17 (2013):9569-9583.
- Bing Bai, Stephen Goodwin, Ken Carlson, Modeling of frac flowback and produced water volume from Wattenberg oil and gas field, Journal of Petroleum Science and Engineering, Volume 108, August 2013, Pages 383-392, ISSN 0920-4105, http://dx.doi.org/10.1016/j.petrol.2013.05.003. (http://www.sciencedirect.com/science/article/pii/S0920410513001344)